Providing quantitative evaluations of friction within a customer experience to reduce abandonment and improve conversion of transactions

1. A method for providing quantitative evaluations of friction within a customer experience to reduce abandonment and improve conversion of transactions, the method comprising: identifying one or more transactions, the one or more transactions including a first transaction; identifying one or more personas corresponding to the one or more transactions, the one or more personas including a first persona corresponding to the first transaction; determining one or more customer friction factor (CFF) scores corresponding to the one or more transactions, the one or more CFF scores including a first CFF score corresponding to the first transaction, the first CFF score being a quantification of an aspect of the first transaction that has a negative impact on a customer experience associated with the first transaction; comparing the one or more CFF scores with one or more valid comparisons including a first comparison, the first comparison relating to the same industry as the first transaction and the same transaction type of the first transaction, the first CFF score being compared to the first comparison; and identifying one or more friction points based on the comparison of the one or more CFF scores, the one or more friction points including a first friction point, the first friction point being associated with the first transaction, wherein addressing the first friction point reduces abandonment and improves conversion associated with transactions.

2. The method of claim 1 , further comprising identifying one or more industries relevant to the customer experience including a first industry.

3. The method of claim 1 , further comprising identifying one or more transaction types corresponding to the one or more transactions, a first transaction type corresponding to the first transaction.

4. The method of claim 3 , wherein retail is an industry associated with the first transaction and wherein the first transaction type relates to one or more of layaway, purchase, return, review, credit application, merchandise pickup, clientelling, delivery, credit payment, gift registry, loyalty program, promotions, coupon matching, or personal shopper.

5. The method of claim 3 , wherein manufacturing is an industry associated with the first transaction and wherein the first transaction type relates to one or more of ordering, billing, accounts payable, accounts receivable, shipping, tracking, campaign management, product feedback, reliability, returns, customer service, or engineering support.

6. The method of claim 3 , wherein banking is an industry associated with the first transaction and wherein the first transaction type relates to one or more of view transaction history, open account, add account type, bill pay, transfers, money order, order checks, direct deposit, cash withdrawal, stop payment, extract transactions for spending analysis, or loan application.

7. The method of claim 3 , wherein public sector is an industry associated with the first transaction and wherein the first transaction type relates to one or more of permitting, enforcement, fines, traffic court, civil court, utility connection, utility billing, driver's license, license plate, inspections, property tax, alarm registration, alarm permitting, communications, or media.

8. The method of claim 1 , further comprising determining one or more proximities corresponding to the one or more transactions, the one or more proximities including a first proximity corresponding to the first transaction.

9. The method of claim 8 , wherein the first proximity includes one or more of online, in person, or on phone transactions.

10. The method of claim 1 , wherein a lower CFF score corresponds to a higher likelihood of customer satisfaction, reduced abandonment, and improved conversion of transactions.

11. The method of claim 1 , wherein a higher CFF score corresponds to a lower likelihood of customer satisfaction, increased abandonment, and reduced conversion of transactions.

12. The method of claim 1 , wherein determining the first CFF score is based on one or more of an industry of the first transaction, a transaction type of the first transaction, a proximity associated with the first transaction, or a persona associated with the first transaction.

13. The method of claim 1 , wherein the first CFF score is associated with the first transaction in its entirety.

14. The method of claim 1 , wherein the first CFF score is associated with one or more aspects of the first transaction including a first aspect.

15. The method of claim 14 , wherein the first aspect is categorized as one or more of engagement, process, technology, ecosystem, or knowledge.

16. The method of claim 1 , wherein the first CFF score is associated with an individual entity involved in the first transaction.

17. The method of claim 1 , wherein the first CFF score is an aggregate score associated multiple entities.

18. The method of claim 1 , further comprising determining one or more valid comparisons including a first comparison, the first comparison being a CFF score other than the first CFF score, the first comparison relating to the same industry as the first transaction and the same transaction type of the first transaction.

19. The method of claim 1 , wherein identifying the one or more personas includes determining a generation of a customer.

20. The method of claim 19 , wherein the generation of the customer is selected from the group consisting of greatest generation, silent generation, baby boomer, Gen X, Millennial, or Boomlets.

21. A non-transitory storage media storing computer-executable instructions for providing quantitative evaluations of friction within a customer experience to reduce abandonment and improve conversion of transactions, the instructions comprising: identifying, with a computer system, one or more transactions, the one or more transactions including a first transaction, wherein identifying one or more transactions comprises obtaining a first log of first logged transactions with a sequence of interactions each having a plurality metrics, a timestamp, and a unique transaction identifier, by interrogating an application program interface (API) of a customer relations management (CRM) database; identifying, with the computer system, one or more personas corresponding to the one or more transactions, the one or more personas including a first persona corresponding to the first transaction, wherein identifying one or more personas comprises clustering groups of transactions in the first logged transactions according to a plurality of dimensions with a density-based clustering algorithm; determining, with the computer system, with means for determining customer friction factor (CFF) scores, one or more CFF scores corresponding to the identified one or more transactions, the one or more CFF scores including a first CFF score corresponding to the first transaction, the first CFF score being a quantification of an aspect of the first transaction that has a negative impact on a customer experience associated with the first transaction; comparing, with the computer system, the one or more CFF scores with one or more valid comparisons including a first comparison, the first comparison relating to the same industry as the first transaction and the same transaction type of the first transaction, the first CFF score being compared to the first comparison; and identifying, with the computer system, one or more friction points based on the comparison of the one or more CFF scores, the one or more friction points including a first friction point, the first friction point being associated with the first transaction, wherein addressing the first friction point reduces abandonment and improves conversion associated with transactions.

22. The media of claim 21 , wherein determining one or more CFF scores comprises determining one or more CFF scores with the product of one or more of the metrics and one or more multipliers determined by: obtaining a training set of second logged transactions with outcomes, the outcomes including some failed transactions; and randomly initializing current multipliers then iteratively, until detecting less than a threshold amount of change in an aggregate amount of error between iterations: determining the aggregate amount of error of predictions of CFF scores produced by the current multipliers relative to outcomes in the training set based on a root mean square error of instances where the CFF score is below a threshold but a corresponding transaction failed; determining partial derivatives of the current multipliers with respect to the aggregate measure of error; and adjusting the current multipliers based on directions that the partial derivatives indicate tend to decrease the aggregate measure of error.

23. The media of claim 22 , wherein determining the multipliers comprises: repeatedly determining sets of candidate multipliers based on different sets of initial multiplier values; and selecting a set of candidate multipliers that is determined to produce a lowest aggregate measure of error among the sets of candidate multipliers.

24. The media of claim 21 , comprising means for account for sequential representations in the log of transactions.